Causal inference and AI/ML in pharmaceutical statistics

Music City Center 

This presentation introduces the basic concepts and fundamental methods of causal inference relevant to pharmaceutical statistics. Starting with the central questions in drug development and licensing and the roles of causal inference and AI/ML in answering them, the presentation consists of three parts: (1) estimand framework, (2) efficient estimators, and (3) targeted learning. The presentation covers causal thinking for different types of commonly used study designs in the pharmaceutical industry, including but not limited to randomized controlled clinical trials, single-arm clinical trials with external controls, and real-world evidence studies. The materials covered in this presentation are extracted from the presenter's book, Causal Inference in Pharmaceutical Statistics, published by Chapman & Hall/CRC in 2024.

Clinical Trials

Estimand

Efficiency

Machine Learning